Lossless image coding has a large variety of important applications, including high quality digital photography, filmography, graphics, etc. It also applies to professional grade video coding, for encoding video frames at the highest possible quality setting, i.e., losslessly. Images in these applications can have diverse characteristics, which presents a difficult challenge for designing an image codec to be generically applicable across these applications. For example, images in graphics typically have sharp edges or transitions in color (e.g., between text and background colors, and at borders of adjacent shapes), whereas photographic images generally are continuous tone (i.e., vary continuously in color (e.g., as a gradient) across the image).
Due to differences in image characteristics, most generic image codecs are either designed to compress photographic (e.g. JPEG) or graphic images (GIF). Photographic image compression usually uses a de-correlating transform like DCT or wavelets, whereas graphic image compression typically uses string based codecs such as LZ77 or LZ78. In general, photographic codecs don't work well on graphics because the basic assumption of local smoothness or DC-bias which underlies transform methods is usually broken in graphics. Conversely, graphics codecs do poorly on photographic images because the alphabet is too large to build a good dictionary. As a result, existing image codecs for photographic images are not designed for easy interoperability with image and video codecs, nor do they handle graphics content efficiently.
For example, CALIC [as described by X. Wu, N. Memon and K. Sayood, “A Context-Based Adaptive Lossless/Nearly-Lossless Coding Scheme For Continuous-Tone Images,” ISO, 1995], JPEG-LS [as described by M. J. Weinberger and G. Seroussi, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Trans. Image Processing, Vol. 9, pp. 1309-1324, August 2000] and SPIHT [as described by Said and W. A. Pearlman, “A New Fast And Efficient Image Codec Based On Set Partitioning In Hierarchical Trees,” IEEE Trans. On Circuits and Systems for Video Technology, Vol. 6, No. 6, pp. 243-250, June 1996] are current state-of-art lossless image codecs for photographic images. However, they are not designed for easy interoperability with image and video codecs, nor do they handle graphics content efficiently. On the other hand, GIF is a current state-of-art lossless graphics codec. But, it too doesn't handle photographic content, nor is it easy to incorporate inside a video codec. PTC [as described by H. S. Malvar, “Fast Progressive Image Coding Without Wavelets,” pp. 243-252, DCC 2000] is a macroblock-based codec that can be easily integrated into image and/or video codecs. However, it does not do very well with graphics content. Further, BTPC [described by J. A. Robinson, “Efficient General-Purpose Image Compression With Binary Tree Predictive Coding,” IEEE Transactions on Image Processing, Vol. 6, No. 4, April 1997] is designed to handle photographic and graphic images in a unified and speed optimized design, but its compression is far from adequate.